Cross compiling RISC V with spike emulation

[nizarzaza]

Hello, I am new to TVM, I have been trying to run with RISC V target but the output always fails to compile.

I tried following the steps here:

[RFC] Enable TVM QNN on RISC-V with Subword SIMD Computation

Hello, we’re the team from NTHU (National Tsing-Hua University), Taiwan. Our team mainly focuses on the design with supporting TVM on RISC-V architecture with SIMD instructions. In this RFC, we target on the application for RISC-V P extension(RVP). This is the extension for RISC-V DSP and subword SIMD extension. Note that a preliminary version of this work is reported at RISC-V Global Forum, Sep. 3, 2020, Lightning talk session(video link). Intro of RISC-V P extension(RVP) RISC-V is an open sou…

but it is failing. I also tried different risc v target and it is still failing.

Is there a guide to be able to generate risc v target from TVM, if not what is the best way to proceed to be able to generate RISC v compatible binaries? Also how to support vector instruction and custom institutions. can we also run it with spike ?

Any suggestion is greatly appreciated

[areusch]

hi @nizarzaza thanks for posting!

the link you posted is a bit old, unfortunately that work didn’t get upstreamed.

this is something we partially support, but would love to improve upon. the best way to get an idea of what TVM generates for RISC-V would be to use the Zephyr qemu_riscv32 board. You could follow the microTVM with TFLite tutorial, but replace the board name with qemu_riscv32. I believe you should be able to get something to compile and run this way.

We do want to support RISC-V including some type of vectorized instructions. We don’t currently have SPIKE included in our CI, but would like to add it, and we have a framework that defines a set of API commands (build, flash, connect) which you could leverage to support SPIKE with microTVM.

Right now, TVM doesn’t have any knowledge of such RISC-V vector instructions, so I wouldn’t expect artifacts for RISC-V to be particularly fast. However, it should be possible to define schedules for RISC-V that leverage those instructions. The main challenge we’ve had is identifying which extension we should support.

let me know if this helps!

Andrew

[nizarzaza]

Thank you @areusch, that was very helpful. I will try to follow the qemu + microTVM guides to see if I can get something running on RISC v.

Another question. If I want to use vectorized instructions including new custom ones. I have to use the schedule API ?

Thank you.

Nizar

[areusch]

Hi Nizar,

Yep that’s the right way to go. You could see how folks have done this for ARM: https://github.com/apache/tvm/pull/9233

Feel free to ask here if you run into difficulties! And, it would be fantastic if you wanted to upstream any progress you make. I can help guide you through the process (or perhaps even just do this for you) of adding SPIKE to the CI if you’d like to leverage that.

Andrew

[nizarzaza]

Hello @areusch hen I was trying to follow this tutorial:

microTVM with TFLite Models — tvm 0.9.dev0 documentation (apache.org)

I am seeing this error:

Traceback (most recent call last): File “tvm_test.py”, line 36, in mod, params = relay.frontend.from_tflite( tflite_model, shape_dict={input_tensor: input_shape}, dtype_dict={input_tensor: input_dtype}) File “/home/zarif/tvm/python/tvm/relay/frontend/tflite.py”, line 3753, in from_tflite op_converter.convert_op_to_relay() File “/home/zarif/tvm/python/tvm/relay/frontend/tflite.py”, line 235, in convert_op_to_relay ret = self.convert_mapop_code_str File “/home/zarif/tvm/python/tvm/relay/frontend/tflite.py”, line 1878, in convert_fully_connected keep_num_dims = fully_connected_options.KeepNumDims() AttributeError: ‘FullyConnectedOptions’ object has no attribute ‘KeepNumDims’

What could the error be?

Thank you,

Nizar

[areusch]

Hi Nizar,

Which version of tflite are you using? I believe we are on 2.6.2 in the TVM CI.

Andrew

[nizarzaza]

I was following the guide so I installed the 2.1.0 version. I tried to update the last version available via pip3 install (2.4.0) but it gave the same error. I will try to build 2.6.2 and see if this fixes the issue.

Nizar

[nizarzaza]

Hello @areusch ,

I have tried to build TFlite my i am still facing the same error.

I am thinking it might be the way I build TVM from source?

What is the config.cmake needed to get microTVM to work ? I am setting the follwing in config.cmake : set(USE_MICRO ON) set(USE_MICRO_STANDALONE_RUNTIME ON) set(USE_LLVM llvm-config-12)

these are the only changes I made when compiling the TVM, or did I miss something?

Thank you,

Nizar

are there other stuff to set to get microTVM to work.

[areusch]

@nizarzaza interesting. we do have a very peculiar way we build TFLite. iiuc it’s due to an API problem. as far as i know folks have been successful using TFLite installed from PyPI though, so I’m not sure if this is needed for particular models or what.

I don’t have cycles to investigate this specifically right now, but I am working on [RFC] Python Dependencies in TVM CI Containers at the moment and as part of that I’ll try to use PyPI-installed TFLite in the CI. I can update this thread as I learn more with that.